A presentation given by Lukas Oberhuber to the APM Planning, Monitoring and Control SIG and guests at the University of Warwick, Coventry 2015. Lukas Oberhuber, Simply Business – technical debt bankruptcy occurs when it’s too late to fix things after the failure. Do things right, don’t take short cuts or else the debt will build up, e.g. Floss your teeth, don’t wait until they fall out, because then it’s too late. Not flossing is the technical debt.

1. technical bankruptcy
lukas oberhuber
simply business

2. who am i?
simply business
online insurance
290,000 policies
45 people on tech team
~60 developing products
~100,000 lines of code increasing rapidly

3. our story
new shareholder with little technology experience
had to explain replacing a 5 year old system: we couldn’t improve the
business anymore
in trying to explain, we had to understand tech debt and tech bankruptcy
(and they weren’t big fans of ‘the software is never done’)

4. our fix
old system took 1 week for rating change
new system 1 hour by product manager

5. conclusion
actual bankruptcy due to technical bankruptcy is likely high
examples
friendster
video management system at the bbc (cancelled)
new air traffic system in the usa
npfit healthcare software programme here at home

6. conclusion
i have equations can help us reason about
how much debt we are accumulating and paying off
how that increases the effort to create change

7. conclusion
same behaviours in business and organisations
examples
signal failures on the london tube poorly flushing toilets at simply business
non-earthquake safe bridges strikes
examples of bankruptcy
iraqi army
garment factory that collapsed in
bangladesh
worldwide corruption

8. conclusion
everything will be couched as software
i will leave to you to transfer this to your domain
hints at the end of how to deal with tech debt

9. but first some definitions

10. definition of technical bankruptcy
economic cost of to change system exceeds needed return for most
changes
even if system is working and stable
implicit: change is needed
implicit: software is never done

11. the problem
usually recognised long after bankruptcy has occurred
it is too late to fix [imagine flossing your teeth vigorously when a root
canal is needed]
businesses fail because of it [example: friendster]

12. definition of technical debt
the work that hasn’t been done
difference between doing something properly and the less correct way it
was actually done
quantified by: amount of work left to correct the implementation vs
original implementation
not: every possible option that could be built

13. problem with technical debt
makes good solutions bad
increases cost of change
starts slowly and picks up speed
eventually, if not paid off, leads to tech bankruptcy

14. intuition around technical debt
principle needs to be paid off
interest accrues
how fast?
how bad?

15. definition of complexity
a unit of the final product
examples
tech
lines of code
rows in a database
function points
business/organisation/othe
r
employees
paragraphs in laws
customers

16. caveat: it’s all a hypothesis
the equations and all else are hypotheses based on empirical,
mathematical information from similar or adjacent areas
this is primarily a thought experiment
therefore further investigation is definitely warranted

17. debt and complexity: two angles
complexity is the base
debt is the accelerant (makes it worse)

18. what does debt look like? the
evidence beyond simply business
debt makes good code bad; what does bad look like?
focusing on maintenance (not exactly the same as adding a line/unit), so
bear with the slight shift
credit: Capers Jones
http://www.compaid.com/caiinternet/ezine/capersjones-maintenance.pdf

19. leading
excellent quality control
very good code structure at the initial release
zero error-prone modules
very low bad-fix injection rate of 1% or less
result: maintenance costs can actually decline over the five year
ownership period

20. average
marginal quality control
reasonable initial code structure
one or two error-prone modules
an average bad-fix injection rate of about 7%
result: maintenance costs increase over a five-year period, but not at a
very significant annual rate

21. lagging
inadequate quality control
poor code structure
up to a dozen severe error-prone modules
significant bad-fix injection rates of about 20%
result: maintenance costs will become more expensive every year due to
entropy and the fact that the application never stabilises

22. maintenance conclusion
in the lagging scenario, ROI is negative in 3-5 years meaning the
system needs to be replaced; this is tech bankruptcy
leading scenario in contrast can have 20 - 30 years of positive ROI

23. an equation that can quantify
complexity and debt
complexity of a system
which equation is the right one?
acceleration of effort due to technical debt
we’ll be looking for the effort to add one unit of complexity

24. complexity of a system

25. options for complexity measures
intuition that complexity of adding a line of code is based on how many other lines of code are
affected
example
write tests
write line of code
change all other places (code or tests) that depend on or are affected by
repeat until finished
we are looking for the ideal (best case) scenario
n will be the amount of complexity

26. http://science.slc.edu/~jmarshall/courses/2002/spring/cs50/BigO/

27. metcalfe’s law - this is a
stretch
value of the nextwork increases by the number of
nodes because they are connected
how does this apply?
more or less n ^ 2
imagine building every connection, not just using it
"Metcalfe-Network-Effect" by Woody993 at en.wikipedia - Transferred from en.wikipedia. Licensed under CC0 via Wikimedia Commons
- http://commons.wikimedia.org/wiki/File:Metcalfe-Network-Effect.svg#mediaviewer/File:Metcalfe-Network-Effect.svg

28. binary trees - another stretch
well known properties of algorithms
number of connections between nodes increases as log n
http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly

29. huge amount of work in software has been done to
compartmentalise to avoid systemic effects
object oriented programming
patterns
the concept of a function or
procedure
libraries of reusable code
layers (like the network layers)
interfaces
protocols
other domains
methodologies for executing organisation change
vast literature on how to run companies more
effectively
All of this allows for building larger and more
complex systems
• lines of code
• number of people

30. base (best case) complexity
good
log(n)
by decoupling and compartmentalising we reduce connections
when a line of code is inserted, it only affects a small part of the network
of connections

31. Text
log n
http://stackoverflow.com/questions/
2307283/what-does-olog-n-mean-
exactly

32. Text

33. technical debt

34. technical debt in complexity terms
tech debt takes log(n) of good code and makes it worse

35. does it accumulate quickly?
see problem, then act
two problems
it’s too late to brush and floss when the teeth are falling out
tech debt accumulates silently, in multiple ways, the slowdown that
results is gradual and then accelerates

36. does it accumulate quickly?
not noticed because decisions are made at multiple levels
there is a multiplier effect causing small concessions to add up quickly
example
management defers needed scope to gain speed
team cuts corners to hit deadlines

37. multiplier effect
management cuts scope team cut corners resulting debt
10%
(90% done right)
10%
(90% done right)
19%
(0.9 * 0.9 = 0.81)
20% 20% 36%
30% 30% 51%
50% 50% 75%

38. together: complexity and debt
log(n)
1
1 - debt

39. together: complexity and debt
log(n)
1
1 - debt

40. debt vs lines of code

41. productivity

42. next steps for the theory
investigation to prove equations are right
better ways to measure debt and complexity

43. what can you do?
if you aren’t scared yet…
otherwise…
symptoms: are you leading, average or lagging?
differences are easily visible
debt ratio needs to be low (but no need to know it exactly)
do things properly
i.e. peer reviews, formal controls, good structure

44. referencesOrganisational change http://www.bristol.ac.uk/media-library/sites/cubec/migrated/documents/pr1.pdf
Groups and scale http://www.shirky.com/writings/group_enemy.html
Big O examples http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly
Godel, Escher, Bach; I Am A Strange Loop; Douglas Hofstadter
Capers Jones; Software Productivity Research Institute
Quality excellence has ROI > $15 for each $1 spent; SOFTWARE QUALITY IN 2012:A SURVEY OF THE STATE OF THE ART http://sqgne.org/presentations/2012-13/Jones-Sep-
2012.pdf
http://www.compaid.com/caiinternet/ezine/capersjones-maintenance.pdf
http://insights.cermacademy.com/2012/05/preventing-software-failure-capers-jones-technologyrisk/
How To Measure Anything; Douglas Hubbard
"Metcalfe-Network-Effect" by Woody993 at en.wikipedia - Transferred from en.wikipedia. Licensed under CC0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Metcalfe-
Network-Effect.svg#mediaviewer/File:Metcalfe-Network-Effect.svg
http://science.slc.edu/~jmarshall/courses/2002/spring/cs50/BigO/
http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly
http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly
Dunbar number http://en.wikipedia.org/wiki/Dunbar%27s_number
Estimates of projects http://www.isbsg.org/
Estimates of projects
http://www.isbsg.org/

45. questions
twitter: @lukasco
email: lukas.oberhuber@simplybusiness.co.uk
linkdedin: uk.linkedin.com/in/lukasoberhube

46. This presentation was delivered
at an APM event
To find out more about upcoming events
please visit our website
www.apm.org.uk/events